{"id":1199,"date":"2023-07-27T07:49:23","date_gmt":"2023-07-27T07:49:23","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-lasso-%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af\/"},"modified":"2023-07-27T07:49:23","modified_gmt":"2023-07-27T07:49:23","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-lasso-%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-lasso-%e1%80%86%e1%80%af%e1%80%90%e1%80%ba%e1%80%9a%e1%80%af%e1%80%90%e1%80%ba%e1%80%99%e1%80%be%e1%80%af\/","title":{"rendered":"Python \u101b\u103e\u102d lasso \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f (\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/my\/lasso-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener noreferrer\">Lasso regression<\/a> \u101e\u100a\u103a data \u1010\u103d\u1004\u103a <a href=\"https:\/\/statorials.org\/my\/multicollinearity-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener noreferrer\">multicollinearity<\/a> \u101b\u103e\u102d\u1014\u1031\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a regression model \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1021\u1010\u102d\u102f\u1001\u103b\u102f\u1015\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1014\u100a\u103a\u1038 \u1006\u102f\u1036\u1038 \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f \u101e\u100a\u103a \u1000\u103b\u1014\u103a\u101b\u103e\u102d\u101e\u1031\u102c \u1005\u1010\u102f\u101b\u1014\u103a\u1038 \u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a (RSS) \u1000\u102d\u102f \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038 \u101c\u103b\u103e\u1031\u102c\u1037\u1001\u103b\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1000\u103c\u102d\u102f\u1038\u1015\u1019\u103a\u1038\u101e\u100a\u103a \u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>i<\/sub> \u2013 \u0177 <sub>i<\/sub> )\u1042<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101b\u103d\u103e\u1031-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u2211<\/strong> : <em>\u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a<\/em> \u101f\u102f \u1021\u1013\u102d\u1015\u1039\u1015\u102c\u101a\u103a\u101b\u101e\u1031\u102c \u1002\u101b\u102d\u101e\u1004\u103a\u1039\u1000\u1031\u1010<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : <sup>\u1021\u102d\u102f\u1004\u103a\u1010\u102e<\/sup> \u101c\u1031\u1037\u101c\u102c\u1001\u103c\u1004\u103a\u1038\u1021\u1010\u103d\u1000\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1010\u1014\u103a\u1016\u102d\u102f\u1038<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : Multiple linear regression model \u1000\u102d\u102f \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1010\u1014\u103a\u1016\u102d\u102f\u1038<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a lasso regression \u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1010\u102d\u102f\u1037\u1000\u102d\u102f \u101c\u103b\u103e\u1031\u102c\u1037\u1001\u103b\u101b\u1014\u103a \u1000\u103c\u102d\u102f\u1038\u1015\u1019\u103a\u1038\u101e\u100a\u103a \u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3|\u03b2 <sub>j<\/sub> |<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\"><em>j \u101e\u100a\u103a<\/em> 1 \u1019\u103e <em>p<\/em> \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u03bb \u2265 0 \u101b\u103e\u102d\u101b\u102c\u101e\u102d\u102f\u1037 \u101e\u103d\u102c\u1038\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u100a\u102e\u1019\u103b\u103e\u1001\u103c\u1004\u103a\u1038\u1010\u103d\u1004\u103a \u1024\u1012\u102f\u1010\u102d\u101a\u1021\u1001\u1031\u102b\u103a\u1021\u101d\u1031\u102b\u103a\u1000\u102d\u102f <em>\u101b\u102f\u1015\u103a\u101e\u102d\u1019\u103a\u1038\u1015\u103c\u1005\u103a\u1012\u100f\u103a<\/em> \u101f\u102f \u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b lasso regression \u1010\u103d\u1004\u103a\u104a \u1016\u103c\u1005\u103a\u1014\u102d\u102f\u1004\u103a\u1001\u103c\u1031\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038 MSE (mean square error) test \u1000\u102d\u102f\u1011\u102f\u1010\u103a\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u03bb \u1021\u1010\u103d\u1000\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a lasso regression \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103a\u103b\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c\u104a Python \u1010\u103d\u1004\u103a lasso regression \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c packages \u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LassoCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 2: \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u102c\u1038 \u1043\u1043 \u1005\u102e\u1038\u101b\u103e\u102d \u1021\u1001\u103b\u1000\u103a\u1021\u101c\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c <strong>mtcars<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c \u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f variable \u1021\u1016\u103c\u1005\u103a <strong>hp \u1000\u102d\u102f<\/strong> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u103c\u102e\u1038 \u1021\u1031\u102c\u1000\u103a\u1015\u102b variable \u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u1030\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1005\u102d\u102f\u1004\u103a\u1038\u1005\u102d\u102f\u1004\u103a\u1038\u1001\u1019\u103a\u1038\u101c\u103e\u102d\u102f\u1004\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1000\u102d\u102f\u101a\u103a\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1015\u103c\u1031\u102c\u101b\u1019\u103e\u102c\u1015\u102b\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1024\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1010\u1004\u103a\u104d \u1015\u103c\u101e\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 3- Lasso Regression Model \u1000\u102d\u102f \u1021\u1036\u1000\u102d\u102f\u1000\u103a\u101c\u102f\u1015\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a sklearn \u104f <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">LassoCV()<\/a> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u103c\u102e\u1038 lasso regression model \u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u101e\u1031\u102c <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u103c\u102e\u1038 \u1015\u103c\u1005\u103a\u1012\u100f\u103a\u1021\u101e\u102f\u1036\u1038\u1021\u1014\u103e\u102f\u1014\u103a\u1038\u1021\u1010\u103d\u1000\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038 \u1021\u101a\u103a\u101c\u103a\u1016\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a RepeatedKFold() \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><em><strong>\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a-<\/strong> &#8220; alpha&#8221;  \u101f\u1030\u101e\u1031\u102c \u1021\u101e\u102f\u1036\u1038\u1021\u1014\u103e\u102f\u1014\u103a\u1038\u1000\u102d\u102f Python \u1010\u103d\u1004\u103a &#8220; lambda&#8221;  \u1021\u1005\u102c\u1038 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/em><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a k = 10 \u1001\u1031\u102b\u1000\u103a\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1015\u103c\u102e\u1038 \u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a\u1021\u1010\u100a\u103a\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u101c\u102f\u1015\u103a\u1004\u1014\u103a\u1038\u1005\u1009\u103a\u1000\u102d\u102f 3 \u1000\u103c\u102d\u1019\u103a\u1015\u103c\u1014\u103a\u101c\u102f\u1015\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">LassoCV() \u101e\u100a\u103a \u1015\u102f\u1036\u1019\u103e\u1014\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1021\u101a\u103a\u101c\u103a\u1016\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038 0,1\u104a 1 \u1014\u103e\u1004\u1037\u103a 10 \u1010\u102d\u102f\u1037\u1000\u102d\u102f\u101e\u102c \u1005\u1005\u103a\u1006\u1031\u1038\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b \u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1000\u102d\u102f\u101a\u103a\u1015\u102d\u102f\u1004\u103a \u1021\u101a\u103a\u101c\u103a\u1016\u102c\u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f 0 \u1019\u103e 1 \u1021\u1011\u102d 0.01 \u1010\u102d\u102f\u1038\u104d \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = LassoCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, n_jobs=<\/strong><\/span> <span style=\"color: #008000;\"><strong>-1<\/strong><\/span> <span style=\"color: #000000;\"><strong>)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u104f MSE \u1000\u102d\u102f\u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038\u101c\u103b\u103e\u1031\u102c\u1037\u1001\u103b\u101e\u1031\u102c lambda \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a <strong>0.99<\/strong> \u1016\u103c\u1005\u103a\u101c\u102c\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 4- \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1015\u1010\u103a\u101e\u1000\u103a\u104d \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038 lasso \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u101b\u100a\u103a\u1021\u1001\u103b\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1000\u102c\u1038\u1021\u101e\u1005\u103a\u1000\u102d\u102f \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1005\u102d\u102f\u1004\u103a\u1038\u1005\u102d\u102f\u1004\u103a\u1038\u1001\u1019\u103a\u1038\u101c\u103e\u102d\u102f\u1004\u103a : \u1042\u1044<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1021\u101c\u1031\u1038\u1001\u103b\u102d\u1014\u103a: 2.5<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1005\u103b\u1031\u1038\u1014\u103e\u102f\u1014\u103a\u1038: 3.5<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec: 18.5<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1024\u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1021\u101e\u1005\u103a\u104f <em>hp<\/em> \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a \u1010\u1015\u103a\u1006\u1004\u103a\u1011\u102c\u1038\u101e\u1031\u102c lasso \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using lasso regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n<\/span>\n<span style=\"color: #000000;\">array([105.63442071])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u1011\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1021\u1015\u1031\u102b\u103a\u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1024\u1000\u102c\u1038\u101e\u100a\u103a <em>\u1019\u103c\u1004\u103a\u1038\u1000\u1031\u102c\u1004\u103a\u101b\u1031<\/em> \u1010\u1014\u103a\u1016\u102d\u102f\u1038 <strong>105.63442071<\/strong> \u101b\u103e\u102d\u1019\u100a\u103a\u101f\u102f \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1010\u103d\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1011\u102c\u1038\u101e\u1031\u102c Python \u1000\u102f\u1012\u103a\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/lasso_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Lasso regression \u101e\u100a\u103a data \u1010\u103d\u1004\u103a multicollinearity \u101b\u103e\u102d\u1014\u1031\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a regression model \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101b\u1014\u103a\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1021\u1010\u102d\u102f\u1001\u103b\u102f\u1015\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1014\u100a\u103a\u1038 \u1006\u102f\u1036\u1038 \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f \u101e\u100a\u103a \u1000\u103b\u1014\u103a\u101b\u103e\u102d\u101e\u1031\u102c \u1005\u1010\u102f\u101b\u1014\u103a\u1038 \u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a (RSS) \u1000\u102d\u102f \u1021\u1014\u100a\u103a\u1038\u1006\u102f\u1036\u1038 \u101c\u103b\u103e\u1031\u102c\u1037\u1001\u103b\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1000\u103c\u102d\u102f\u1038\u1015\u1019\u103a\u1038\u101e\u100a\u103a \u104b RSS = \u03a3(y i \u2013 \u0177 i )\u1042 \u101b\u103d\u103e\u1031- \u2211 : \u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a \u101f\u102f \u1021\u1013\u102d\u1015\u1039\u1015\u102c\u101a\u103a\u101b\u101e\u1031\u102c \u1002\u101b\u102d\u101e\u1004\u103a\u1039\u1000\u1031\u1010 y i : \u1021\u102d\u102f\u1004\u103a\u1010\u102e \u101c\u1031\u1037\u101c\u102c\u1001\u103c\u1004\u103a\u1038\u1021\u1010\u103d\u1000\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1010\u1014\u103a\u1016\u102d\u102f\u1038 \u0177 i : Multiple linear regression [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u101b\u103e\u102d Lasso Regression (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a) - Statorials<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a Python \u101b\u103e\u102d lasso regression \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/my\/python-\u1010\u103d\u1004\u103a-lasso-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u101b\u103e\u102d Lasso Regression (\u1010\u1005\u103a\u1006\u1004\u1037\u103a\u1015\u103c\u102e\u1038\u1010\u1005\u103a\u1006\u1004\u1037\u103a) - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a 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